Research on Intelligent Campus and Visual Teaching System Based on Internet of Things

Abstract

The rapid development of Internet of things technology provides robust conditions for building a perfect intelligent campus. A visual teaching question answering system is essential for creating a smart campus, significantly improving education quality. However, the accuracy of the existing teaching question answering system is not high. To solve this problem, this paper proposes a visual teaching system based on a knowledge map. The system mainly includes two parts: problem processing and answer search. In the part of problem processing, combined with the pretraining language model, a new model framework is constructed to deal with the problem of entity reference recognition, entity link, and relationship extraction. By setting three kinds of classification labels, the problem is divided into simple, chain, and multientity problems. Different solutions are given to the above three classification problems in the answer search part. The experimental results show that the answer accuracy of this system is higher than other comparison methods.